The aim of this study was to identify typical and specific features of land surface temperature (LST) distribution in the city of Krakow and its surroundings with the use of Landsat/ETM+ data. The paper contains a detailed description of the study area and technical properties of the Landsat program and data, as well as a complete methodology of LST retrieval. Retrieved LST records have been standardized in order to ensure comparability between satellite images acquired during different seasons. The method also enables identification of characteristic thermal regions, i.e. areas always colder and always warmer than a zonal mean LST value for Krakow. The research includes spatial analysis of the standardized LST with regard to different land cover types. Basic zonal statistics such as mean standardized LST and percentage share of hot and cold regions within 10 land cover types were calculated. GIS was used for automated data processing and mapping. The results confirmed the most obvious dependence of the LST on different land cover types. Some more factors influencing the LST were recognized on the basis of detailed investigation of the LST pattern in the urban agglomeration of Krakow. The factors are: emission of anthropogenic heat, insolation of the surfaces depending first of all on land relief and shape of buildings, seasonal changes of vegetation and weather conditions at the time of satellite image acquisition.

In cities located in concave landforms, urban heat island (UHI) is an element of a complicated thermal structure and occurs due to the common impact of urban built-up areas and orography-induced processes like katabatic flows or air temperature inversions. Kraków, Poland (760,000 inhabitants) is located in a large valley of the river Vistula. In the years 2009–2013, air temperature was measured with the 5-min sampling resolution at 21 urban and rural points, located in various landforms. Cluster analysis was used to process data for the night-time. Sodar and synoptic data analysis provided results included in the definition of the four types of night-time thermal structure representing the highest and the lowest spatial air temperature variability and two transitional types. In all the types, there are three permanent elements which show the formation of the inversion layer, the cold air reservoir and the UHI peak zone. As the impact of land use and relief on air temperature cannot be separated, a concept of relief-modified UHI (RMUHI) was proposed as an alternative to the traditional UHI approach. It consists of two steps: (1) recognition of the areal thermal structure taking into consideration the city centre as a reference point and (2) calculation of RMUHI intensity separately for each vertical zone.

The paper presents an additional ArcGIS Desktop 10 toolbox for automatic retrieval of brightness temperature (Ts), land surface emissivity (LSE) and land surface temperature (LST) from LANDSAT data. The LANDSAT TRS (Thermal Remote Sensing) Tools employ land surface temperature estimation procedure developed by J. C. Jimenez-Munoz and J. A. Sobrino [1, 2, 3 and 4]. Detailed description of this algorithm is included. Special attention is paid to practical use of the land surface temperature maps derived from LANDSAT data in terms of environmental research. Numerous examples are referred to and detailed description of LANDSAT land surface temperature maps application for thermal condition research in urban areas is provided. A sample LST map of agglomeration of Krakow in Southern Poland is included.

Urban areas are among those most endangered with the potential global climate changes. The studies concerning the impact of global changes on local climate of cities are of a high significance for the urban inhabitants' health and wellbeing. This paper is the final report of a project (Urban climate in Central European cities and global climate change) with the aim to raise the public awareness on those issues in five Central European cities: Szeged (Hungary), Brno (Czech Republic), Bratislava (Slovakia), Kraków (Poland) and Vienna (Austria). Within the project, complex data concerning local geomorphological features, land use and long-term climatological data were used to perform the climate modelling analyses using the model MUKLIMO_3 provided by the German Weather Service (DWD).